Dual photon microscopy and its potential applications in NASH

Arun J. Sanyal MBBS, MD

Professor of Medicine, Physiology and Molecular Pathology

Virginia Commonwealth University School of Medicine

Conflicts of Interest

• President, Sanyal Biotechnologies • Stock options: Genfit, Akarna, Tiziana, Indalo, Durect,

Exhalenz, Hemoshear • Advisor with compensation: Lilly, Pfizer, Novartis,

Ardelyx, Salix, Hemoshear • Advisor without compensation: Galectin, Intercept,

Merck, Bristol Myers, Immuron, Gilead, Chemomab, Affimmune, Protalix, Nitto Denko, Novo Nordisk, Cirius, Boehringer Ingelhiem

• Grants to institution: Gilead, Tobira, Allergan, Merck, Bristol Myers, Astra Zeneca, Immuron, Intercept, Novo Nordisk, Shire, Boehringer Ingelhiem, Cirius

Histological Staging of NASH

1 2

3 4

Limitations of traditional methods of histological staging of fibrosis

• Susceptibility to sampling variability

• Intra- and Inter-observer variability

• Ordinal classification of a continuous process

• Limited dynamic scale

Workflow for Dual Photon Microscopy of the liver

biopsy Scan of unstained

Tissue section (4µm) Readout:

70+ collagen fibril parameters

Dual photon scan of the liver in NASH F0

F1

F2

F3

F4

Dual photon microscopy permits measurement of collagen content and characteristics

Collagen Quantification – Collagen Strings Morphometrics

a. Area b. Perimeter c. Compactness - d. Length e. Breadth f. Convex Hull Perimeter g. Convex Hull Area

h. Elongation i. Roughness j. Major Axis Length k. Minor Axis Length l. Orientation m. Axis Ratio n. Tortuosity

Courtesy-Genesis Imaging Services

Utility of histological methods of assessment

Comparability (Q, SE, SP)

Applicability (Regulatory)

Output

Presence

Location

Extent

Dynamic scale

Semiquantitative staging

Collagen proportionate area

Quantitative profiling of FP

Yes Yes Yes

Yes No Yes

Descriptive ordinals

Quantitative value

Quantitative parameters

Limited

Continuous

?

Descriptive estimation of

fibrosis pattern and dynamics

Quantitative calculation of fibrosis

dynamics

Quantitative calculation of

fibrosis pattern and dynamics

Standard N/A ?

Routine practice, clinical study

Experimental, clinical study

Experimental, clinical study

Establishment and Cross-sectional Validation of a New Tool for NAFLD

10 Wang Y, et al. Hepatology 2017 doi:10.1002/hep.29090.

Basic characteristics

Variable Test cohort (n=50) Validation cohort (n=42)

n (%) or median (range)

Age (year) 56 (34-75) 57 (36-80)

Gender (male) 10 (20) 7 (17)

BMI (kg/m2) 33.8 (22.7-43.0) 34.4 (22.2-45.1)

BMI >30 kg/m2 40 (80) 34 (81)

Diabetes 21 (42) 20 (48)

Hyperlipidemia 32 (64) 30 (71)

ALT (U/L) 54 (13-255) 84 (24-248)

AST/ALT ratio 0.9 (0.5-4.3) 1.0 (0.4-3.7)

Biopsy length (mm) 16.5 (8.0-72.0) 15.5 (6.0-52.0)

Steatosis

1 25 (50) 17 (40)

2 13 (26) 13 (31)

3 12 (24) 12 (29)

Inflammation

0 2 (4) 2 (5)

1 26 (52) 32 (76)

2 22 (44) 8 (19)

3 0 (0) 0 (0)

Ballooning

0 15 (30) 7 (17)

1 26 (52) 23 (55)

2 9 (18) 12 (29)

11 Wang et al, Hepatology 2017

Distribution of fibrosis stage

Variable Test cohort (n=50) Validation cohort (n=42)

N (%)

Fibrosis stage

0 9 (18) 5 (12)

1 12 (24) 13 (31)

2 12 (24) 10 (24)

3 7 (14) 12 (29)

4 10 (20) 2 (5)

12 Wang et al, Hepatology 2017

Quantitative fibrosis parameters (qFP) change with increasing fibrosis stage

Wang Y, et al. Hepatology 2017 doi:10.1002/hep.29090.

A total of 70 q-FPs: inter-observer agreement= 0.97±0.03 intra-observer agreement= 0.98±0.03

Sampling size variance:

qFP measures differences in collagen fibrillar properties even in stage 1 disease

Visualization of changes in qFP with increasing fibrosis stage

Wang Y, et al. Hepatology 2017 doi:10.1002/hep.29090.

Q-FPs with excellent performance for independently reflecting progressing NASH fibrosis

Discrimination Q-FP AUROC 95% CI Sensitivity Specificity

F0 vs F1-4 NoStr 0.87 0.75-0.99 0.84 0.78

StrEccentricity 0.87 0.75-0.99 0.84 0.78

StrSolidity 0.87 0.75-0.99 0.84 0.78

StrLengthV 0.88 0.75-1.00 0.86 0.78

F0,1 vs F2-4 NoStr 0.81 0.68-0.93 0.81 0.71

StrEccentricity 0.81 0.68-0.93 0.81 0.71

StrSolidity 0.81 0.69-0.93 0.81 0.71

StrLengthV 0.83 0.71-0.95 0.68 0.86

F0-2 vs F3,4 NoStr 0.89 0.79-0.98 0.90 0.82

StrEccentricity 0.89 0.79-0.98 0.90 0.82

StrSolidity 0.89 0.79-0.98 0.90 0.82

StrLengthV 0.86 0.76-0.97 0.84 0.76

F0-3 vs F4 NoStr 0.92 0.84-1.00 0.90 0.81

StrEccentricity 0.92 0.84-1.00 0.90 0.81

StrSolidity 0.92 0.84-1.00 0.90 0.81

StrLengthV 0.91 0.82-1.00 0.90 0.83

OR 8.5, p=0.004 OR 8.3, p=0.004

OR 8.0, p=0.003 OR 12.0, p=0.02

16

Distribution of qFPs capture differences between fibrosis stages with fidelity

Wang Y, et al. Hepatology 2017 doi:10.1002/hep.29090.

Use of desirability functions in complex medical sciences

Good

Bad

DF = (d1 x d2 x … x dk)

1/k

Desirability functions provide a clear method to predict mortality in patients with cirrhosis

NASTRA population Veterans Hospital population

N= 1332 N=105

Genning et al, J Hepatology, 2012

Establish Desirability function for dynamic scaling of NASH fibrosis

q-FP Desirability Index= (d1 x d2 x …x d4)1/4

20

Wang Y, et al. Hepatology 2017 doi:10.1002/hep.29090.

Differential changes in portal predominant fibrosis vs central-central fibrosis in stage 4 NASH

Wang Y, et al. Hepatology 2017 doi:10.1002/hep.29090.

Baseline Fibrosis Signature That Could Be More Reversible following antiviral therapy for HBV

22

Sun J, et al. Hepatology 2014;59:1283. Xu S & Wang Y, et al. J Hepatol 2014;61:260. Wang Y, et al. Sci Rep 2017.

Changes in qFP over time

Courtesy- Genesis Imaging Services

Future directions

• Cross-validation across cohorts

• Assessment of changes over time and early prediction of cirrhosis

• Assessment of changes vs clinical outcomes

Acknowledgements

Prof. Yan Wang (SMU)

Prof. Jinlin Hou (SMU)

Prof. Qianjin Feng (SMU)

Drs Robert Vincent (VCU), Wei Yang (SMU), Xieer Liang (SMU), et al

Graduate students in SMU: Jinlian Yang, Zhipeng Liu, Jiaen Liang, et al

26

RO1 DK 10596

National Natural Science Foundation of China (81670522, 81371603), Guangdong Province Science & Technology Plan Project (2013B051000051), National Science & Technology Major Project (2012ZX10002003), and Guangzhou Science & Technology Plan Project (201607020019).